# Object-Detection-API

**Repository Path**: dyjch/Object-Detection-API

## Basic Information

- **Project Name**: Object-Detection-API
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-02-20
- **Last Updated**: 2021-02-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Object-Detection-API

A web-app that provides object detection using YOLOv3 and also an API.

It's implemented using django framework and PyTorch (for YOLO model). The app uses cloudinary API for image hosting.<br>

![Maintenance](https://img.shields.io/badge/Maintained%3F-Yes-green.svg) ![GitHub pull-requests](https://img.shields.io/github/issues-pr/atharva-18/Object-Detection-API.svg) ![GitHub issues](https://img.shields.io/github/issues/atharva-18/Object-Detection-API.svg) <br>
![GitHub contributors](https://img.shields.io/github/contributors/atharva-18/Object-Detection-API.svg) ![Generic badge](https://img.shields.io/badge/Python-3.7.3-Blue.svg)

### Dependencies

<ul>
    <li> 
        <a href="https://www.djangoproject.com/" >Django</a>
    </li>
    <li> 
        <a href="https://pytorch.org/" >PyTorch</a>
    </li>
    <li>
        <a href="https://pillow.readthedocs.ioenstable" >Pillow</a>
    </li>
    <li>
        <a href="https://opencv.org/" >OpenCV</a>
    </li>
</ul>

You also need to download the `yolo.weights` file and place it in the "weights" directory.

You can download the weights by - 
```
    $ wget https://pjreddie.com/media/files/yolov3.weights
```
### Usage

Firstly, you need to put your cloudinary credentials (cloud_name, api_key and api_secret) in ```settings.py``` file.

To run the server
```
    $ pip3 install -r requirements.txt
    $ python3 manage.py collectstatic
    $ python3 manage.py runserver
```

The website is hosted at - https://object-detection-yolo.herokuapp.com

The detection service is currently not working because of memory limits of Heroku.

### Web API

To use the web API, you can send a POST request to - https://object-detection-yolo.herokuapp.com/detect/api

#### Input

You can send either of the following parameters -

Parameter | Type                           | Description
--------- | ------------------------------ | ---------------------------------------------------------------------------------
image     | file                           | Image file that you want to detect.
image64   | text                           | Image in base64 form that you want to detect. Currently supports JPEG images only

#### Result

Parameter | Type                | Description
--------- | ------------------- | -----------------------------------------------
success   | bool                | Whether classification was successful or not 
detect    | class label, float  | pair of label and its confidence
url       | image URL           | Link to the result image uploaded to cloudinary

Example:  {"success": true, "detect": {  "dog": 0.9989, "truck": 0.9999 }, 'url':"https://example.com/image.png"}<br>
"detect" will be empty if no objects are detected.

### Example

The website also shows the detection output with bounding boxes around the detected objects. There will be no box if the input doesn't contain any object.

#### Input 

![Dog](temp.png)

#### Output

![Output](result.png)

No. of objects detected - 3 <br>

The result image is generated using matplotlib.

### TO-DO

<ul>
    <li>Move app to Google Cloud Platform</li>
    <li>Create a docker image</li>
</ul>

### Contribute

If you want to contribute and/or find any bug, feel free to do a pull request!